Systems immunology: an intro to multi-omics data integration and machine learning CLASSROOM
Monday, 27 June 2022 to Tuesday, 28 June 2022, 9.30am - 3pm
***This course is fully booked*** Planning to use machine learning to better understand your data? In this interactive course, we will learn how to use machine learning for biomedical data integration and knowledge discovery.
You are expected to attend two sessions:
27 June @ 9:30 - 15:00
28 June @ 9:30 - 15:00
Please note that there will be no lunch/refreshments provided.
In the Systems immunology: an intro to multi-omics data integration and machine learning course, we will learn how to perform integrative analysis using SIMON, our recently developed machine learning approach. In SIMON, analysis is performed using an intuitive graphical user interface and standardized, automated machine learning approach allowing non-technical researchers to identify patterns and extract knowledge from high-dimensional data and build thousands of high-quality predictive models using 180+ machine learning algorithms. SIMON helps to identify optimal algorithms and provides a resource that empowers non-technical and technical researchers to identify crucial patterns in biomedical data.
The course is aimed at biomedical researchers with minimal or no machine learning experience, but with background knowledge and hands-on experience in collecting and analysing ‘omics’ data, such as transcriptomics, proteomics, cytometry and other single-cell data analysis and planning to perform integrative analysis. By the end of this course users should be able to perform SIMON analysis of their own data including data preparation and exploration analysis.
- complete end-to-end machine learning analysis using SIMON
- learn how to prepare data for analysis
- understand the importance of reducing the dimensionality using appropriate methods
- learn how to properly evaluate predictive models using performance metrics
- perform exploratory analysis
HOW IT WILL WORK
This is 2-day interactive course comprised of theoretical and practical parts. Users will receive a link and joining instructions beforehand. Users are required to install SIMON software 3 days before the course, download datasets and read the original manuscripts describing SIMON (Tomic et al, JI, 2019 and Tomic et al, Patterns, 2021). The sessions are divided into two parts: theoretical and practical, with breaks for questions and to help users to complete the practical exercises.
Day 1 - SIMON, pattern recognition and knowledge discovery platform for integrative analysis
- Theoretical part: Introduction to machine learning
- Theoretical part (+ case study): Introduction to SIMON approach
- Practical hands-on sessions: perform SIMON analysis using provided dataset, performance metrics, evaluation, and selection of high-quality models
Day 2 - Exploratory analysis
- Theoretical + practical part: feature selection - scoring and elimination
- Theoretical + practical part: correlation and clustering analysis
- Theoretical part (+ case study): Feature selection and processing methods
- Discussion about project-specific problems (share your datasets or research questions before the course)
WHAT YOU WILL NEED
The prerequisites for this course are:
- Install SIMON software **3 days before the course**
è installation instructions: https://github.com/genular/simon-frontend#installation-quickstart
Please note: if you fail to install SIMON software 3 days before the course, we will have to give your space to students on the waiting list
- Participants for this course are required to have background knowledge in ‘omics’ data, such as transcriptomics, proteomics, cytometry and other single-cell data analysis, and preferentially should already have omics datasets on which they are planning to perform integrative analysis
- Read publications:
- Tomic et al, JI, 2019, https://doi.org/10.4049/jimmunol.1900033
- Tomic et al, Patterns, 2021, https://doi.org/10.1016/j.patter.2020.100178
- If you have a specific project you are working on and would like to perform SIMON analysis on that dataset or just get an input on how to prepare data for the SIMON analysis, please contact instructor before the course, and we might accommodate discussion about your project during the sessions
- Step-by-step analysis instructions: SIMON manuscript (link: https://www.cell.com/patterns/fulltext/S2666-3899(20)30242-7)
- Instruction videos (link: https://genular.org/simon-machine-learning-knowledge-base/instruction-videos/)
ATTENDANCE SURVEY ON COMPLETION
It is now a requirement that you complete the three short questions in the survey you receive after attending the course. Once you have submitted the survey, you will be sent an email with a link to your attendance certificate. This is to ensure we receive the feedback we need to evaluate and improve our courses. Survey results are downloaded and stored anonymously.
Where no cost is indicated in the shopping trolley, no deposit is required. However, two or more non-attendances or late cancellations without good reason will be logged and may mean you cannot attend any further MSD training that term. Please refer to our Terms and Conditions for further information.